






"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   :							List[str]          =		{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Dict		=					"""detr"""
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					["""past_key_values"""]
    SCREAMING_SNAKE_CASE		:	Dict		=					{
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }






    def __init__(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		__SCREAMING_SNAKE_CASE	:				int=None			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				int=100			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=6			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=2048			,		__SCREAMING_SNAKE_CASE	:				int=8			,		__SCREAMING_SNAKE_CASE	:				List[str]=6			,		__SCREAMING_SNAKE_CASE	:				int=2048			,		__SCREAMING_SNAKE_CASE	:				int=8			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.0			,		__SCREAMING_SNAKE_CASE	:				List[str]=0.0			,		__SCREAMING_SNAKE_CASE	:				Tuple=True			,		__SCREAMING_SNAKE_CASE	:				List[Any]="relu"			,		__SCREAMING_SNAKE_CASE	:				List[str]=256			,		__SCREAMING_SNAKE_CASE	:				str=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.0			,		__SCREAMING_SNAKE_CASE	:				List[str]=0.0			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				int=1.0			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=False			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="sine"			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="resnet50"			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				List[str]=False			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=1			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				Any=2			,		__SCREAMING_SNAKE_CASE	:				Any=1			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=1			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=5			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=0.1			,		**__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		)	->						Union[str, Any]:
      if backbone_config is not None and use_timm_backbone:
        raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

      if not use_timm_backbone:
        if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
          lowerCamelCase_       	=		CONFIG_MAPPING['resnet'](out_features=['stage4']   )
        elif isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		backbone_config.get('model_type'   )
          lowerCamelCase_       	=		CONFIG_MAPPING[backbone_model_type]
          lowerCamelCase_       	=		config_class.from_dict(__SCREAMING_SNAKE_CASE   )
        # set timm attributes to None
        lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		None, None, None

      lowerCamelCase_       	=		use_timm_backbone
      lowerCamelCase_       	=		backbone_config
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		num_queries
      lowerCamelCase_       	=		d_model
      lowerCamelCase_       	=		encoder_ffn_dim
      lowerCamelCase_       	=		encoder_layers
      lowerCamelCase_       	=		encoder_attention_heads
      lowerCamelCase_       	=		decoder_ffn_dim
      lowerCamelCase_       	=		decoder_layers
      lowerCamelCase_       	=		decoder_attention_heads
      lowerCamelCase_       	=		dropout
      lowerCamelCase_       	=		attention_dropout
      lowerCamelCase_       	=		activation_dropout
      lowerCamelCase_       	=		activation_function
      lowerCamelCase_       	=		init_std
      lowerCamelCase_       	=		init_xavier_std
      lowerCamelCase_       	=		encoder_layerdrop
      lowerCamelCase_       	=		decoder_layerdrop
      lowerCamelCase_       	=		encoder_layers
      lowerCamelCase_       	=		auxiliary_loss
      lowerCamelCase_       	=		position_embedding_type
      lowerCamelCase_       	=		backbone
      lowerCamelCase_       	=		use_pretrained_backbone
      lowerCamelCase_       	=		dilation
      # Hungarian matcher
      lowerCamelCase_       	=		class_cost
      lowerCamelCase_       	=		bbox_cost
      lowerCamelCase_       	=		giou_cost
      # Loss coefficients
      lowerCamelCase_       	=		mask_loss_coefficient
      lowerCamelCase_       	=		dice_loss_coefficient
      lowerCamelCase_       	=		bbox_loss_coefficient
      lowerCamelCase_       	=		giou_loss_coefficient
      lowerCamelCase_       	=		eos_coefficient
      super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    @property
    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      return self.encoder_attention_heads






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						int:
      return self.d_model






    @classmethod
    def   UpperCamelCase			(			cls	:				str			,		__SCREAMING_SNAKE_CASE	:				PretrainedConfig			,		**__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						Optional[int]:
      return cls(backbone_config=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict[str, any]:
      lowerCamelCase_       	=		copy.deepcopy(self.__dict__   )
      if output["backbone_config"] is not None:
        lowerCamelCase_       	=		self.backbone_config.to_dict()
      lowerCamelCase_       	=		self.__class__.model_type
      return output






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Dict		=					version.parse("""1.11"""						)






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ('pixel_mask', {0: 'batch'}),
          ]   )






    @property
    def   UpperCamelCase			(			self	:				int   )	->						float:
      return 1e-5






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						int:
      return 12












"""simple docstring"""





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)






@dataclass
class 	a		:
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=1_2_8    , metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=__snake_case    , metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=__snake_case    , metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=__snake_case    , metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                """value if set."""
            )
        }    , )






@dataclass
class 	a		:
    SCREAMING_SNAKE_CASE		:	str		=					field(
        default=__snake_case    , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
    SCREAMING_SNAKE_CASE		:	str		=					field(
        default=__snake_case    , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Train language if it is different from the evaluation language."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    , )
    SCREAMING_SNAKE_CASE		:	Optional[bool]		=					field(
        default=__snake_case    , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}    , )
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}    , )
    SCREAMING_SNAKE_CASE		:	str		=					field(
        default="""main"""    , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    , )
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}    , )







def 	lowerCamelCase__			(				) ->							Any:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  lowerCamelCase_       	=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
  lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry('run_xnli'						,							_lowerCamelCase					)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,							datefmt='%m/%d/%Y %H:%M:%S'						,							handlers=[logging.StreamHandler(sys.stdout					)]						,							)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  lowerCamelCase_       	=		training_args.get_process_log_level()
  logger.setLevel(_lowerCamelCase					)
  datasets.utils.logging.set_verbosity(_lowerCamelCase					)
  transformers.utils.logging.set_verbosity(_lowerCamelCase					)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + F'''distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'''					)
  logger.info(F'''Training/evaluation parameters {training_args}'''					)

  # Detecting last checkpoint.
  lowerCamelCase_       	=		None
  if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
    lowerCamelCase_       	=		get_last_checkpoint(training_args.output_dir					)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          'Use --overwrite_output_dir to overcome.'					)
    elif last_checkpoint is not None:
      logger.info(
          F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Set seed before initializing model.
  set_seed(training_args.seed					)

  # In distributed training, the load_dataset function guarantees that only one local process can concurrently
  # download the dataset.
  # Downloading and loading xnli dataset from the hub.
  if training_args.do_train:
    if model_args.train_language is None:
      lowerCamelCase_       	=		load_dataset(
          'xnli'						,							model_args.language						,							split='train'						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    else:
      lowerCamelCase_       	=		load_dataset(
          'xnli'						,							model_args.train_language						,							split='train'						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    lowerCamelCase_       	=		train_dataset.features['label'].names

  if training_args.do_eval:
    lowerCamelCase_       	=		load_dataset(
        'xnli'						,							model_args.language						,							split='validation'						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    lowerCamelCase_       	=		eval_dataset.features['label'].names

  if training_args.do_predict:
    lowerCamelCase_       	=		load_dataset(
        'xnli'						,							model_args.language						,							split='test'						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    lowerCamelCase_       	=		predict_dataset.features['label'].names

  # Labels
  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Load pretrained model and tokenizer
  # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowerCamelCase_       	=		AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							num_labels=_lowerCamelCase						,							idalabel={str(_lowerCamelCase					): label for i, label in enumerate(_lowerCamelCase					)}						,							labelaid={label: i for i, label in enumerate(_lowerCamelCase					)}						,							finetuning_task='xnli'						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
  lowerCamelCase_       	=		AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,							do_lower_case=model_args.do_lower_case						,							cache_dir=model_args.cache_dir						,							use_fast=model_args.use_fast_tokenizer						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
  lowerCamelCase_       	=		AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path						,							from_tf=bool('.ckpt' in model_args.model_name_or_path					)						,							config=_lowerCamelCase						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,							)

  # Preprocessing the datasets
  # Padding strategy
  if data_args.pad_to_max_length:
    lowerCamelCase_       	=		'max_length'
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    lowerCamelCase_       	=		False

  def preprocess_function(_lowerCamelCase			:						Any					):
    # Tokenize the texts
    return tokenizer(
        examples['premise']						,							examples['hypothesis']						,							padding=_lowerCamelCase						,							max_length=data_args.max_seq_length						,							truncation=_lowerCamelCase						,							)

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      lowerCamelCase_       	=		min(len(_lowerCamelCase					)						,							data_args.max_train_samples					)
      lowerCamelCase_       	=		train_dataset.select(range(_lowerCamelCase					)					)
    with training_args.main_process_first(desc='train dataset map pre-processing'					):
      lowerCamelCase_       	=		train_dataset.map(
          _lowerCamelCase						,							batched=_lowerCamelCase						,							load_from_cache_file=not data_args.overwrite_cache						,							desc='Running tokenizer on train dataset'						,							)
    # Log a few random samples from the training set:
    for index in random.sample(range(len(_lowerCamelCase					)					)						,							3					):
      logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''					)

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      lowerCamelCase_       	=		min(len(_lowerCamelCase					)						,							data_args.max_eval_samples					)
      lowerCamelCase_       	=		eval_dataset.select(range(_lowerCamelCase					)					)
    with training_args.main_process_first(desc='validation dataset map pre-processing'					):
      lowerCamelCase_       	=		eval_dataset.map(
          _lowerCamelCase						,							batched=_lowerCamelCase						,							load_from_cache_file=not data_args.overwrite_cache						,							desc='Running tokenizer on validation dataset'						,							)

  if training_args.do_predict:
    if data_args.max_predict_samples is not None:
      lowerCamelCase_       	=		min(len(_lowerCamelCase					)						,							data_args.max_predict_samples					)
      lowerCamelCase_       	=		predict_dataset.select(range(_lowerCamelCase					)					)
    with training_args.main_process_first(desc='prediction dataset map pre-processing'					):
      lowerCamelCase_       	=		predict_dataset.map(
          _lowerCamelCase						,							batched=_lowerCamelCase						,							load_from_cache_file=not data_args.overwrite_cache						,							desc='Running tokenizer on prediction dataset'						,							)

    # Get the metric function
  lowerCamelCase_       	=		evaluate.load('xnli'					)

  # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(_lowerCamelCase			:						EvalPrediction					):
    lowerCamelCase_       	=		p.predictions[0] if isinstance(p.predictions						,							_lowerCamelCase					) else p.predictions
    lowerCamelCase_       	=		np.argmax(_lowerCamelCase						,							axis=1					)
    return metric.compute(predictions=_lowerCamelCase						,							references=p.label_ids					)

  # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
  if data_args.pad_to_max_length:
    lowerCamelCase_       	=		default_data_collator
  elif training_args.fpaa:
    lowerCamelCase_       	=		DataCollatorWithPadding(_lowerCamelCase						,							pad_to_multiple_of=8					)
  else:
    lowerCamelCase_       	=		None

  # Initialize our Trainer
  lowerCamelCase_       	=		Trainer(
      model=_lowerCamelCase						,							args=_lowerCamelCase						,							train_dataset=train_dataset if training_args.do_train else None						,							eval_dataset=eval_dataset if training_args.do_eval else None						,							compute_metrics=_lowerCamelCase						,							tokenizer=_lowerCamelCase						,							data_collator=_lowerCamelCase						,							)

  # Training
  if training_args.do_train:
    lowerCamelCase_       	=		None
    if training_args.resume_from_checkpoint is not None:
      lowerCamelCase_       	=		training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCamelCase_       	=		last_checkpoint
    lowerCamelCase_       	=		trainer.train(resume_from_checkpoint=_lowerCamelCase					)
    lowerCamelCase_       	=		train_result.metrics
    lowerCamelCase_       	=		(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowerCamelCase					)
    )
    lowerCamelCase_       	=		min(_lowerCamelCase						,							len(_lowerCamelCase					)					)

    trainer.save_model()  # Saves the tokenizer too for easy upload

    trainer.log_metrics('train'						,							_lowerCamelCase					)
    trainer.save_metrics('train'						,							_lowerCamelCase					)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    logger.info('*** Evaluate ***'					)
    lowerCamelCase_       	=		trainer.evaluate(eval_dataset=_lowerCamelCase					)

    lowerCamelCase_       	=		data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowerCamelCase					)
    lowerCamelCase_       	=		min(_lowerCamelCase						,							len(_lowerCamelCase					)					)

    trainer.log_metrics('eval'						,							_lowerCamelCase					)
    trainer.save_metrics('eval'						,							_lowerCamelCase					)

  # Prediction
  if training_args.do_predict:
    logger.info('*** Predict ***'					)
    lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		trainer.predict(_lowerCamelCase						,							metric_key_prefix='predict'					)

    lowerCamelCase_       	=		(
        data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_lowerCamelCase					)
    )
    lowerCamelCase_       	=		min(_lowerCamelCase						,							len(_lowerCamelCase					)					)

    trainer.log_metrics('predict'						,							_lowerCamelCase					)
    trainer.save_metrics('predict'						,							_lowerCamelCase					)

    lowerCamelCase_       	=		np.argmax(_lowerCamelCase						,							axis=1					)
    lowerCamelCase_       	=		os.path.join(training_args.output_dir						,							'predictions.txt'					)
    if trainer.is_world_process_zero():
      with open(_lowerCamelCase						,							'w'					) as writer:
        writer.write('index\tprediction\n'					)
        for index, item in enumerate(_lowerCamelCase					):
          lowerCamelCase_       	=		label_list[item]
          writer.write(F'''{index}\t{item}\n'''					)


if __name__ == "__main__":
  main()




